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I'm using the nlsLM function to fit a nonlinear regression. How does one extract the hat values and Cook's Distance from an nlsLM model object?

With objects created using the nls or nlreg functions, I know how to extract the hat values and the Cook's Distance of the observations, but I can't figure out how to get them using nslLM.

Can anyone help me out on this? Thanks!

Bajcz
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Karl_R
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1 Answers1

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So, it's not Cook's Distance or based on hat values, but you can use the function nlsJack in the nlstools package to jackknife your nls model, which means it removes every point, one by one, and bootstraps the resulting model to see, roughly speaking, how much the model coefficients change with or without a given observation in there.

Reproducible example:

xs = rep(1:10, times = 10)
ys = 3 + 2*exp(-0.5*xs)
for (i in 1:100) {
xs[i] = rnorm(1, xs[i], 2)
}
df1 = data.frame(xs, ys)
nls1 = nls(ys ~ a + b*exp(d*xs), data=df1, start=c(a=3, b=2, d=-0.5))
require(nlstools)
plot(nlsJack(nls1))

The plot shows the percentage change in each model coefficient as each individual observation is removed, and it marks influential points above a certain threshold as "influential" in the resulting plot. The documentation for nlsJack describes how this threshold is determined:

An observation is empirically defined as influential for one parameter if the difference between the estimate of this parameter with and without the observation exceeds twice the standard error of the estimate divided by sqrt(n). This empirical method assumes a small curvature of the nonlinear model.

My impression so far is that this a fairly liberal criterion--it tends to mark a lot of points as influential.

nlstools is a pretty useful package overall for diagnosing nls model fits though.

Bajcz
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